normalization.diagnostic | R Documentation |
Plot comparative Box-Whisker and Heatmap plots of variables across samples check the effectiveness of normalization before and after Mean-Variance Regularization.
normalization.diagnostic(obj, pal, title = "Normalization Diagnostic Plots", device = NULL, file = "Normalization Diagnostic Plots", path = getwd(), horizontal = FALSE, width = 7, height = 8, ...)
obj |
Object of class" |
title |
Title of the plot. Defaults to "Normalization Diagnostic Plots". |
pal |
Color palette. |
device |
Graphic display device in {NULL, "PS", "PDF"}. Defaults to NULL (standard output screen). Currently implemented graphic display devices are "PS" (Postscript) or "PDF" (Portable Document Format). |
file |
File name for output graphic. Defaults to "Normalization Diagnostic Plots". |
path |
Absolute path (without final (back)slash separator). Defaults to working directory path. |
horizontal |
|
width |
|
height |
|
... |
Generic arguments passed to other plotting functions. |
Option file
is used only if device is specified (i.e. non NULL
).
The argument pal
can be any color palette, e.g. as provided by R package RColorBrewer.
None. Displays the plots on the chosen device
.
This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health (P30-CA043703).
End-user function.
"Jean-Eudes Dazard, Ph.D." jean-eudes.dazard@case.edu
"Hua Xu, Ph.D." huaxu77@gmail.com
"Alberto Santana, MBA." ahs4@case.edu
Maintainer: "Jean-Eudes Dazard, Ph.D." jean-eudes.dazard@case.edu
Dazard J-E. and J. S. Rao (2010). "Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data." In JSM Proceedings, Section for High-Dimensional Data Analysis and Variable Selection. Vancouver, BC, Canada: American Statistical Association IMS - JSM, 5295-5309.
Dazard J-E., Hua Xu and J. S. Rao (2011). "R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization." In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863.
Dazard J-E. and J. S. Rao (2012). "Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data." Comput. Statist. Data Anal. 56(7):2317-2333.
justvsn
(R package vsn) Variance stabilization and calibration for microarray data.
loess
(R package stats) Fit a polynomial surface determined by one or more numerical predictors, using local fitting.
## Not run: #=================================================== # Loading the library and its dependencies #=================================================== library("MVR") library("RColorBrewer") #=================================================== # MVR package news #=================================================== MVR.news() #================================================ # MVR package citation #================================================ citation("MVR") #=================================================== # Loading of the Synthetic and Real datasets # (see description of datasets) #=================================================== data("Synthetic", "Real", package="MVR") ?Synthetic ?Real #=================================================== # Mean-Variance Regularization (Real dataset) # Multi-Group Assumption # Assuming unequal variance between groups # Without cluster usage #=================================================== nc.min <- 1 nc.max <- 30 probs <- seq(0, 1, 0.01) n <- 6 GF <- factor(gl(n = 2, k = n/2, length = n), ordered = FALSE, labels = c("M", "S")) mvr.obj <- mvr(data = Real, block = GF, log = FALSE, nc.min = nc.min, nc.max = nc.max, probs = probs, B = 100, parallel = FALSE, conf = NULL, verbose = TRUE, seed = 1234) #=================================================== # Summary Normalization Diagnostic Plots (Real dataset) # Multi-Group Assumption # Assuming unequal variance between groups #=================================================== normalization.diagnostic(obj = mvr.obj, title = "Normalization Diagnostic Plots (Real - Multi-Group Assumption)", pal = brewer.pal(n=11, name="RdYlGn"), device = NULL, horizontal = FALSE, width = 7, height = 8) ## End(Not run)
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